Laser & Optoelectronics Progress, Volume. 58, Issue 8, 0828002(2021)

Estimation Model of Chlorophyll-a Concentration Based on Continuous Wavelet Coefficient

Yongshi Peng1,2,3, Shuisen Chen3、**, Jinyue Chen3, Jing Zhao3, Chongyang Wang3, and Yunlan Guan1,2、*
Author Affiliations
  • 1Faculty of Geomatics, East China University of Technology, Nanchang, Jiangxi 330013, China
  • 2Key Laboratory of Watershed Ecology and Geographical Environment Monitoring National Administration of Surveying, Mapping and Geoinformation,Nanchang, Jiangxi 330013, China
  • 3Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangdong Engineering Technology Center for Remote Sensing Big Data Application, Guangzhou Institute of Geography, Guangzhou, Guangdong 510070, China
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    Figures & Tables(10)
    Sampling areas and sampling stations
    Statistical characteristics of chlorophyll-a in different datasets
    Analysis of water reflectance curve and its single-band correlation
    Diagram of RSI and NDCI correlation coefficient matrix. (a) RSI; (b) NDCI
    Correlation coefficient diagram of db4 wavelet coefficient and chlorophyll-a concentration
    Predicted chlorophyll-a concentration based on sym6 wavelet transform and measured value
    • Table 1. Ten selected mother wavelet bases of continuous wavelet transform and their application fields

      View table

      Table 1. Ten selected mother wavelet bases of continuous wavelet transform and their application fields

      Mother waveletResearch objectReference
      rbio3.3bior3.3sym7db1Maize (chlorophyll, carotenoids)Wang Z L, et al. (2020)[20]
      db5haarmexhmorlWinter wheat (chlorophyll)He R Y, et al. (2018)[21]
      mexhZizania caduciflora (chlorophyll)Yu Z X, et al. (2018)[22]
      db4Broadleaf shrub,small tree (chlorophyll)Fang S H, et al. (2015)[23]
      sym6Maize (chlorophyll)Liu H J, et al. (2018)[24]
    • Table 2. Statistical characteristic of chlorophyll-a concentration at sampling points

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      Table 2. Statistical characteristic of chlorophyll-a concentration at sampling points

      Sample setNumber /(μg·L-1)Minimum /(μg·L-1)Median /(μg·L-1)Maximum /(μg·L-1)Mean /(μg·L-1)SD /(μg·L-1)CV /(μg·L-1)
      Calibration dataset480.43413.10050.33818.51014.5000.783
      Validation dataset210.4829.40044.87317.70616.7710.947
      All dataset690.43412.70050.33818.26515.1060.827
    • Table 3. Common chlorophyll-a concentration inversion models and their modeling accuracy

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      Table 3. Common chlorophyll-a concentration inversion models and their modeling accuracy

      Spectral indexModelVariablerR2RMSE/ (μg·L-1)
      ReflectanceR1R1=712 nm0.3280.15315.063
      RSIR1R2R1=654 nmR2=644 nm0.7210.6449.760
      NDCIR1-R2R1+R2R1=658 nmR2=632 nm0.7210.6699.414
      Three-band(R1-1-R2-1R3R1=649 nmR2=653 nmR3=675 nm0.7360.6809.254
    • Table 4. Accuracy evaluation of CWT-PLSR model

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      Table 4. Accuracy evaluation of CWT-PLSR model

      Mother waveletPCRc2RMSEC/ (μg·L-1)Rp2RMSEP/ (μg·L-1)RPD
      rbio3.330.7007.8640.63510.8301.551
      bior3.340.5519.6130.6629.5451.759
      sym740.5939.1560.7178.7251.924
      db150.6568.4180.36714.7021.142
      db550.7277.4910.7259.5301.762
      haar90.6927.9660.39914.4721.160
      mexh10.50710.0770.56010.9551.533
      morl40.7567.0810.8128.2352.039
      db470.7796.7510.66810.9741.530
      sym630.7327.4310.7326.4572.600
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    Yongshi Peng, Shuisen Chen, Jinyue Chen, Jing Zhao, Chongyang Wang, Yunlan Guan. Estimation Model of Chlorophyll-a Concentration Based on Continuous Wavelet Coefficient[J]. Laser & Optoelectronics Progress, 2021, 58(8): 0828002

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    Paper Information

    Category: Remote Sensing and Sensors

    Received: Oct. 15, 2020

    Accepted: Nov. 12, 2020

    Published Online: Apr. 16, 2021

    The Author Email: Shuisen Chen (css@gdas.ac.cn), Yunlan Guan (guan8098@163.com)

    DOI:10.3788/LOP202158.0828002

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